The aim of this paper is to evaluate and prioritize the proposed cost-effectiveness criteria in supply chain management using fuzzy multiple attribute decision-making (MADM) approach. Over the past few years, the determination of suitable cost-effectiveness criteria in the supply chain has become a key strategic issue. However, the nature of these kinds of decisions is usually complex and unstructured. Many quantitative and qualitative factors must be considered to determine the suitable criteria. As the human decision-making process usually contains fuzziness and vagueness, a hierarchy of MADM model based on fuzzy-sets theory is used in this research. Using a fuzzy analytic hierarchy process (FAHP), the weights of criteria and subcriteria are determined and then the final ranking is determined by technique for order preference by similarity to ideal solution (TOPSIS). Finally, fuzzy TOPSIS (FTOPSIS) is employed to compare the results with classic TOPSIS. This paper concludes that the subcriteria in all the items are in the same rank. 1. Introduction Supply chain plays a critical role for a company to gain competitive advantage, since the supply chain affects customer service, inventory and distribution costs, and response to the ever-changing markets directly. Furthermore, this role becomes more critical in today’s distributed manufacturing environment in which companies focus on core competencies and outsource supportive tasks which in turn create large supply networks. In an international marketplace, an increasingly tough competition results in a company’s attempt to find strategies which give them more competitive advantage than their rivals [1]. In fact, the competition is among supply chains, not companies [2]. Typically, supply chain inventory management studies are classified into three stages, which are supply, production, and distribution [3]; however, the main focus is usually placed on the coordination between only two of them. The main objective of this research is to propose a systematic evaluation model which eases the way for manufacturing companies to select and find the most important cost-effective criteria in supply chain management (SCM) under fuzzy multievaluator and multicriteria environment. Hence, this study utilizes multicriteria decision-making method to determine the importance weights of evaluation criteria in linguistic terms parameterized with triangular fuzzy numbers. This approach is employed for four reasons: (a) it is rational and understandable; (b) the computation processes are straightforward; (c) the concept
References
[1]
M. Christopher, Logistics and Supply Chain Management, Pearson Education, Harlow, UK, 1998.
[2]
S. Li, B. Ragu-Nathan, S. Subba Rao, and T. S. Ragu-Nathan, “The impact of supply chain management practices on competitive advantage and organizational performance,” Omega, vol. 34, no. 2, pp. 107–124, 2006.
[3]
W. A. Jauhari, “Integrated inventory model for three-layer supply chains with stochastic demand,” International Journal of Operational Research, vol. 13, no. 3, pp. 295–317, 2012.
[4]
H. Deng, C. H. Yeh, and R. J. Willis, “Inter-company comparison using modified TOPSIS with objective weights,” Computers and Operations Research, vol. 27, no. 10, pp. 963–973, 2000.
[5]
D. L. Olson, “Comparison of weights in TOPSIS models,” Mathematical and Computer Modelling, vol. 40, no. 7-8, pp. 721–727, 2004.
[6]
E. H. Sabri and B. M. Beamon, “A multi-objective approach to simultaneous strategic and operational planning in supply chain design,” Omega, vol. 28, no. 5, pp. 581–598, 2000.
[7]
M. A. Cohen and H. L. Lee, “Resource development analysis of global manufacturing and distribution networks,” Journal of Manufacturing and Operation Management, vol. 2, pp. 81–104, 1989.
[8]
D. Petrovic, R. Roy, and R. Petrovic, “Modelling and simulation of a supply chain in an uncertain environment,” European Journal of Operational Research, vol. 109, no. 2, pp. 299–309, 1998.
[9]
H. Pirkul and V. Jayaraman, “A multi-commodity, multi-plant, capacitated facility location problem: formulation and efficient heuristic solution,” Computers and Operations Research, vol. 25, no. 10, pp. 869–878, 1998.
[10]
C. J. Vidal and M. Goetschalck, “Modeling the effect of uncertainties on global logistics systems,” Journal of Business Logistics, vol. 21, no. 1, pp. 95–121, 2000.
[11]
Y. H. Lee and S. H. Kim, “Production-distribution planning in supply chain considering capacity constraints,” Computers and Industrial Engineering, vol. 43, no. 1-2, pp. 169–190, 2002.
[12]
H. Yin, J. Zheng, and X. Wang, “Multi-agent based supply chain modeling and bidding,” in Proceedings of the 5th World Congress on Intelligent Control and Automation (WCICA '04), pp. 3187–3191, Hangzhou, China, June 2004.
[13]
F. Nonino and R. Panizzolo, “Integrated production/distribution planning in the supply chain: the Febal case study,” Supply Chain Management, vol. 12, no. 2, pp. 150–163, 2007.
[14]
A. Ross and V. Jayaraman, “An evaluation of new heuristics for the location of cross-docks distribution centers in supply chain network design,” Computers and Industrial Engineering, vol. 55, no. 1, pp. 64–79, 2008.
[15]
V. Jayaraman and A. Ross, “A simulated annealing methodology to distribution network design and management,” European Journal of Operational Research, vol. 144, no. 3, pp. 629–645, 2003.
[16]
B. J. Jeong, H. S. Jung, and N. K. Park, “A computerized causal forecasting system using genetic algorithms in supply chain management,” Journal of Systems and Software, vol. 60, no. 3, pp. 223–237, 2002.
[17]
Y. Yu, H. Chen, and F. Chu, “A new model and hybrid approach for large scale inventory routing problems,” European Journal of Operational Research, vol. 189, no. 3, pp. 1022–1040, 2008.
[18]
M. E. Seliaman, “Using complete squares method to optimize replenishment policies in a four-stage supply chain with planned backorders,” Advances in Decision Sciences, vol. 2011, Article ID 745896, 9 pages, 2011.
[19]
A. Amirteimoori and L. Khoshandam, “A data envelopment analysis approach to supply chain efficiency,” Advances in Decision Sciences, vol. 2011, Article ID 608324, 8 pages, 2011.
[20]
C. L. Hwang and K. Yoon, Multiple Attribute Decision Making Methods and Application, Springer, New York, NY, USA, 1981.
[21]
C. T. Chen, “Extensions of the TOPSIS for group decision-making under fuzzy environment,” Fuzzy Sets and Systems, vol. 114, no. 1, pp. 1–9, 2000.
[22]
Y. M. Wang and T. M. S. Elhag, “Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment,” Expert Systems with Applications, vol. 31, no. 2, pp. 309–319, 2006.
[23]
T. C. Wang and T. H. Chang, “Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment,” Expert Systems with Applications, vol. 33, no. 4, pp. 870–880, 2007.
[24]
T. Yang and C. C. Hung, “Multiple-attribute decision making methods for plant layout design problem,” Robotics and Computer-Integrated Manufacturing, vol. 23, no. 1, pp. 126–137, 2007.
[25]
T. C. Wang and H. D. Lee, “Developing a fuzzy TOPSIS approach based on subjective weights and objective weights,” Expert Systems with Applications, vol. 36, no. 5, pp. 8980–8985, 2009.
[26]
C. Kahraman, G. Büyük？zkan, and N. Y. Ate？, “A two phase multi-attribute decision-making approach for new product introduction,” Information Sciences, vol. 177, no. 7, pp. 1567–1582, 2007.
[27]
M. Da？deviren, S. Yavuz, and N. Kilin？, “Weapon selection using the AHP and TOPSIS methods under fuzzy environment,” Expert Systems with Applications, vol. 36, no. 4, pp. 8143–8151, 2009.
[28]
A. T. Gumus, “Evaluation of hazardous waste transportation firms by using a two step fuzzy-AHP and TOPSIS methodology,” Expert Systems with Applications, vol. 36, no. 2, pp. 4067–4074, 2009.
[29]
C. C. Sun and G. T. R. Lin, “Using fuzzy TOPSIS method for evaluating the competitive advantages of shopping websites,” Expert Systems with Applications, vol. 36, no. 9, pp. 11764–11771, 2009.
[30]
F. Torfi and A. Rashidi, “Selection of project managers in construction Firms using analytic hierarchy process (AHP) and fuzzy Topsis: a case study,” Journal of Construction in Developing Countries, vol. 16, no. 1, pp. 69–89, 2011.
[31]
E. Armero, M. S. García-Cascales, M. D. Gómez-López, and M. T. Lamata, “Decision making in uncertain rural scenarios by means of fuzzy TOPSIS method,” Advances in Decision Sciences, vol. 2011, Article ID 937092, 15 pages, 2011.
[32]
S. J. Chen and C. L. Hwang, Fuzzy Multiple Attribute Decision Making, vol. 375 of Lecture Notes in Economics and Mathematical System Series, Springer, New York, NY, USA, 1992.
[33]
G. Svensson, “Holistic and cross-disciplinary deficiencies in the theory generation of supply chain management,” Supply Chain Management, vol. 8, no. 4, pp. 303–316, 2003.
[34]
C. Y. Wong, J. S. Arlbj？rn, and J. Johansen, “Supply chain management practices in toy supply chains,” Supply Chain Management, vol. 10, no. 5, pp. 367–378, 2005.
[35]
M. E. Johnson, “Learning from toys: lessons in managing supply chain risk from the toy industry,” California Management Review, vol. 43, no. 3, pp. 106–124, 2001.
[36]
M. Christopher, R. Lowson, and H. Peck, “Creating agile supply chains in the fashion industry,” International Journal of Retail & Distribution Management, vol. 32, no. 8, pp. 367–376, 2004.
[37]
I. Ertu？rul and N. Karaka？o？lu, “Performance evaluation of Turkish cement firms with fuzzy analytic hierarchy process and TOPSIS methods,” Expert Systems with Applications, vol. 36, no. 1, pp. 702–715, 2009.
